Abstract
Applied optimization problems such as design, identification, design of controlled systems, operational development of prototypes, analysis of large-scale systems, and forecasting from observational data are multicriteria problems in essence. Construction of the feasible solution set is of primary importance in the above problems. The definition of a feasible solution set is usually considered to be the skill of a designer. Even though this skill is essential, it is by no means sufficient for the correct statement of the problem. There are many antagonistic performance criteria and all kinds of constraints in these problems; therefore, it is quite difficult to correctly determine the feasible set. As a result, ill-posed problems are solved, and optimal solutions are searched for far from where they should be. As a consequence, the optimization results have no practical meaning. In this work we propose methods and tools that will assist the designer in defining the feasible solution set correctly.
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